In transmitting digitally-coded representations of visual images, such as in television, it is desirable from a cost standpoint to minimize the number of bits of information transmitted without materially degrading image quality. Reducing the number of bits reduces the required channel bandwidth, the dominant cost factor in communication systems. Visual images are transmitted by first producing an analog video signal composed of a voltage level proportional to measured luminance at each of many small picture elements uniformly distributed across the image and then digitizing the analog signal by assigning the closest quantization level to each picture element. Generally if too few quantization levels are used, the received picture exhibits so-called quantization noise, e.g., coarse and unpleasing contours caused by rounding off intermediate luminance values to the closest quantization level.
One technique for avoiding the contours, suggested in Roberts, L. G. "PCM Television Bandwidth Reduction Using Pseudo-Random Noise", Thesis, M.I.T. Dept. of Electrical Engineering, February 1961, is to add pseudo-random noise having a maximum amplitude of plus or minus half a quantization level to the digitized signal before quantization and subtract the noise thereafter. This tends to randomize the rounding off process and replace the well-defined contours with regions wherein there are frequent random shifts between quantization levels, producing a perceived gradual variation between levels. This procedure effectively replaces the quantizing noise by a more acceptable random noise throughout the image.
Another procedure is to use spatial filters to separate the video signal into high and low spatial frequency components to allow subsampling and fine quantization of the lows channel, thus avoiding the spurious contours, and to allow coarser quantization of the highs channel, where more quantization noise is tolerable because noise introduced there shows up for the most part in high detail areas where it is less visible. Very coarse quantization of the highs channel, i.e., four to eight levels, does, however, leave visible artifacts.
Another procedure is to compress (nonlinearly attenuate) the luminance signal before quantization and expand it after to convert it to what is known as a lightness scale on which the human eye has approximately uniform sensitivity to luminance perturbations and noise, thus assuring that quantizing noise will be equally visible in both dark and light portions of the picture. The same procedure, sometimes referred to as tapered quantization, has also been used for a similar purpose in a separate high-frequency channel, making quantizing noise more equally visible in both low and high contrast areas.
Finally, differential quantization or DPCM is used to transmit essentially the differential of the video signal, with the receiver integrating to restore the original signal. DPCM does not exhibit the spurious contours of coarsely-quantized PCM, given the same number of bits/sample. However, in designing differential systems a trade off is necessary between the ability to accurately reproduce sharp edges in a picture without blurring and the intensity of echoes at these edges and the amount of image granularity caused by quantization errors in sending low-frequency information in differential form.
Another related technique for improving picture quality is to emphasize edge contrast by what is known as sharpening, in which the high-frequency component described previously is amplified before it is added to the lows channel. Because sharpening tends to emphasize inherent noise in a picture and also to produce its own artifact bands on either side of over-sharpened edges, it is known to make the degree of sharpening dependent on local luminance and contrast, generally providing more sharpening in bright than in dark areas and in midrange rather than very low or high contrast areas. This avoids noise emphasis in areas of uniform luminance where noise is most visible, and it avoids sharpening already sharp edges. Curlander, P. J., "Image Enhancement Using Digital Adaptive Filtering", Thesis, M.I.T. Dept. of Electrical Engineering, August 1977.
An article by E. R. Kretzmer, "Reduced-Alphabet Representation of Television Signals", Conv. Rec., Vol. 4 pp. 140-153, 1956, shows dividing the video signal into low and high-frequency components (or more than two components), coarsely quantizing with tapered step sizes the high-frequency component, and finely quantizing and subsampling the low-frequency component. Low-pass filters and subtractors were suggested as a means of producing the separate components. Kretzmer recognized that picture areas of fine detail are more immune to the effects of quantizing than are areas of uniform-luminance.